When the training data are insufficient, especially when only a small sample size of data is available, domain knowledge will be taken into the process of learning parameters to improve the performance of the Bayesian...When the training data are insufficient, especially when only a small sample size of data is available, domain knowledge will be taken into the process of learning parameters to improve the performance of the Bayesian networks. In this paper, a new monotonic constraint model is proposed to represent a type of common domain knowledge. And then, the monotonic constraint estimation algorithm is proposed to learn the parameters with the monotonic constraint model. In order to demonstrate the superiority of the proposed algorithm, series of experiments are carried out. The experiment results show that the proposed algorithm is able to obtain more accurate parameters compared to some existing algorithms while the complexity is not the highest.展开更多
The accuracy of target threat estimation has a great impact on command decision-making.The Bayesian network,as an effective way to deal with the problem of uncertainty,can be used to track the change of the target thr...The accuracy of target threat estimation has a great impact on command decision-making.The Bayesian network,as an effective way to deal with the problem of uncertainty,can be used to track the change of the target threat level.Unfortunately,the traditional discrete dynamic Bayesian network(DDBN)has the problems of poor parameter learning and poor reasoning accuracy in a small sample environment with partial prior information missing.Considering the finiteness and discreteness of DDBN parameters,a fuzzy k-nearest neighbor(KNN)algorithm based on correlation of feature quantities(CF-FKNN)is proposed for DDBN parameter learning.Firstly,the correlation between feature quantities is calculated,and then the KNN algorithm with fuzzy weight is introduced to fill the missing data.On this basis,a reasonable DDBN structure is constructed by using expert experience to complete DDBN parameter learning and reasoning.Simulation results show that the CF-FKNN algorithm can accurately fill in the data when the samples are seriously missing,and improve the effect of DDBN parameter learning in the case of serious sample missing.With the proposed method,the final target threat assessment results are reasonable,which meets the needs of engineering applications.展开更多
基金supported by the National Natural Science Foundation of China(6130513361573285)the Fundamental Research Funds for the Central Universities(3102016CG002)
文摘When the training data are insufficient, especially when only a small sample size of data is available, domain knowledge will be taken into the process of learning parameters to improve the performance of the Bayesian networks. In this paper, a new monotonic constraint model is proposed to represent a type of common domain knowledge. And then, the monotonic constraint estimation algorithm is proposed to learn the parameters with the monotonic constraint model. In order to demonstrate the superiority of the proposed algorithm, series of experiments are carried out. The experiment results show that the proposed algorithm is able to obtain more accurate parameters compared to some existing algorithms while the complexity is not the highest.
基金supported by the Fundamental Scientific Research Business Expenses for Central Universities(3072021CFJ0803)the Advanced Marine Communication and Information Technology Ministry of Industry and Information Technology Key Laboratory Project(AMCIT21V3).
文摘The accuracy of target threat estimation has a great impact on command decision-making.The Bayesian network,as an effective way to deal with the problem of uncertainty,can be used to track the change of the target threat level.Unfortunately,the traditional discrete dynamic Bayesian network(DDBN)has the problems of poor parameter learning and poor reasoning accuracy in a small sample environment with partial prior information missing.Considering the finiteness and discreteness of DDBN parameters,a fuzzy k-nearest neighbor(KNN)algorithm based on correlation of feature quantities(CF-FKNN)is proposed for DDBN parameter learning.Firstly,the correlation between feature quantities is calculated,and then the KNN algorithm with fuzzy weight is introduced to fill the missing data.On this basis,a reasonable DDBN structure is constructed by using expert experience to complete DDBN parameter learning and reasoning.Simulation results show that the CF-FKNN algorithm can accurately fill in the data when the samples are seriously missing,and improve the effect of DDBN parameter learning in the case of serious sample missing.With the proposed method,the final target threat assessment results are reasonable,which meets the needs of engineering applications.
